Search Results for author:
Found 16 papers, 7 papers with code
SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering
To that end, in this work, we mathematically and empirically reveal the limitation of existing attribute bias removal methods in presence of strong bias and propose a new method that can mitigate this limitation.
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks
Ensuring a neural network is not relying on protected attributes (e. g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI.
Shadow Datasets, New challenging datasets for Causal Representation Learning
Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling.
Trojan Model Detection Using Activation Optimization
TRIGS delivers the best performance on the new dataset, surpassing the baseline methods by a large margin.
A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
In this paper, we investigate two challenging conditions for environment misalignment: Cross-Domain and Cross-Context by proposing four datasets that are designed for these challenges: SimB-Border, SimB-Split, BlenB-Border, and BlenB-Split.
Explaining Face Presentation Attack Detection Using Natural Language
Due to the limited amount of annotated data in our study, we apply a light-weight LSTM network as our natural language generation model.
Introducing the DOME Activation Functions
The function can also be extended to the case of multi-class classification, and used as an alternative to the standard softmax function.
Arabic Scene Text Recognition in the Deep Learning Era: Analysis on A Novel Dataset
Therefore, we use our new dataset to evaluate the problem of Arabic scene text recognition from three perspectives: (1) using deep learning techniques and studying their suitability for Arabic scene text recognition, where we identify essential components required for the model to obtain good performance; (2) identifying Arabic text challenges that differ from Latin text and require special attention; (3) investigating a bilingual model that concurrently deals with Arabic and English words, since Arabic text is usually found along with other languages.
SCAN: Sequence-character Aware Network for Text Recognition
Alternatively, it can also be posed as a character prediction problem.
Ensemble-based Adversarial Defense Using Diversified Distance Mapping
We propose an ensemble-based defense against adversarial examples using distance map layers (DMLs).
MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization
We also show that MUSCLE has the potential to boost the classification performance when used in the fine-tuning phase for a model pre-trained only on unlabeled data.
Input Fast-Forwarding for Better Deep Learning
This scheme is substantially different from "deep supervision" in which the loss layer is re-introduced to earlier layers.
An Image Dataset of Text Patches in Everyday Scenes
Although much research has been devoted to text detection and recognition in scanned documents, relatively little attention has been given to text detection in other types of images, such as photographs that are posted on social-media sites.
Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets
It identifies the sub-interval with the maximum classifier score in linear time.
AlexU-Word: A New Dataset for Isolated-Word Closed-Vocabulary Offline Arabic Handwriting Recognition
The end goal is to collect a very large dataset of segmented letter images, which can be used to build and evaluate Arabic handwriting recognition systems that are based on segmented letter recognition.
Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A Comparative Study on a Novel Dataset
This paper presents a comparative study for window-based descriptors on the application of Arabic handwritten alphabet recognition.
